A computational investigation into nickel-bis(diselenolene) complexes as potential catalysts for reduction of H<sup>+</sup> to H<sub>2</sub>
Bibliographic record
Abstract
As a result of burning fossil fuels, levels of greenhouse gases in our atmosphere are increasing at an alarming rate. Such an increase in greenhouse gases threatens our planet due to global climate change. To reduce the production of greenhouse gases, we must switch from fossil fuels to alternative fuels for energy. The most viable alternative energy source involves the conversion of solar energy into chemical energy via the photocatalytic splitting of water to form molecular hydrogen. In the present work, the Ni-bis(1,2-diamine-diselenolene) and Ni-bis(1,2-dicyano-diselenolene) complexes were studied using density functional theory (DFT). From the results, it was found that the 1,2-diamine-diselenolene and 1,2-dicyano-diselenolene nickel complexes catalyze the formation of H2(g) with overall reaction Gibbs energies of +8.7 kJ mol–1 and +8.4 kJ mol–1, respectively, in a dilute aqueous environment versus the standard hydrogen electrode (SHE). Although both are able to catalyze the HER through a marginally endergonic reaction, the most thermodynamically favourable pathways differed between the complexes. In particular, the most thermodynamically favourable pathway for the formation of H2 by CNOx involves an EECC mechanism, whereas for NH2Ox, the most thermodynamically favourable pathway occurs via an ECCE mechanism. From the results presented, the choice of substituent on the alkene backbone significantly affects the reduction potential and reaction Gibbs energies of protonation. The considerably more positive reduction potential for the CN complexes may offer a solution to the problems experimentally observed for the production of H2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".